Acta Biotheoretica

, Volume 66, Issue 1, pp 61–78 | Cite as

Prediction of Apoptosis Protein’s Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information

  • Yunyun Liang
  • Shengli Zhang
Regular Article


The apoptosis protein has a central role in the development and the homeostasis of an organism. Obtaining information about the subcellular localization of apoptosis protein is very helpful to understand the apoptosis mechanism and the function of this protein. Prediction of apoptosis protein’s subcellular localization is a challenging task, and currently the existing feature extraction methods mainly rely on the protein’s primary sequence. In this paper we develop a feature extraction model based on two different descriptors of evolutionary information, which contains the 192 frequencies of triplet codons (FTC) in the RNA sequence derived from the protein’s primary sequence and the 190 features from a detrended forward moving-average cross-correlation analysis (DFMCA) based on a position-specific scoring matrix (PSSM) generated by the PSI-BLAST program. Hence, this model is called FTC-DFMCA-PSSM. A 382-dimensional (382D) feature vector is constructed on the ZD98, ZW225 and CL317 datasets. Then a support vector machine is adopted as classifier, and the jackknife cross-validation test method is used for evaluating the accuracy. The overall prediction accuracies are further improved by an objective and rigorous jackknife test. Our model not only broadens the source of the feature information, but also provides a more accurate and reliable automated calculation method for the prediction of apoptosis protein’s subcellular localization.


Subcellular localization Triplet codon Position-specific scoring matrix Detrended moving-average cross-correlation Support vector machine 



This work was supported by the National Natural Science Foundation of China (No. 11601407), the Fundamental Research Funds for the Central Universities (Nos. JB160711 and JBG160703) and Doctoral Scientific Research Foundation of Xi’an Polytechnic University (No. BS1710).

Compliance with Ethical Standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ScienceXi’an Polytechnic UniversityXi’anPeople’s Republic of China
  2. 2.School of Mathematics and StatisticsXidian UniversityXi’anPeople’s Republic of China

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